class_docstr: Contains unit tests for the TransportNNDistanceComputer class. 
docstr: Returns true if one network has a corresponding layer in the other and vice
  versa. 
docstr_tok: ["Returns","true","if","one","network","has","a","corresponding","layer","in","the","other","and","vice","versa","."]
code: def _has_corresponding_layers(cls, cost_matrix):
      """Returns true if one network has a corresponding layer in the other and vice
      versa."""
      ret = True
      for row_idx in range(cost_matrix.shape[0]):
          ret = ret and np.any(cost_matrix[row_idx, :] == 0)
      for col_idx in range(cost_matrix.shape[1]):
          ret = ret and np.any(cost_matrix[:, col_idx] == 0)
      return ret
  
imports: import numpy
  from .None import otmann
  from .unittest_neural_network import generate_cnn_architectures, generate_mlp_architectures
  from ..utils.ancillary_utils import get_list_of_floats_as_str
  from ..utils.base_test_class import BaseTestClass, execute_tests
label_desc: Use image node_id 5 for calling the TransportNNDistanceComputerTestCase obj's underlying member method code with example usage: obj._has_corresponding_layers(cls, cost_matrix) and returns: ret